Jose M. Alonso

[16]Cloverphene: a Clover-Shaped cata-Condensed Nanographene with Sixteen Fused Benzene Rings.

Abstract Cloverphenes are graphene-type polyarenes, the structures of which resemble the threefold symmetry of clover leafs. In their Communication (10.1002/anie.201104935), D. Pena and co-workers present the synthesis and properties of a nanosized [16]cloverphene derivative, which consists of 16 fused benzene rings (22 benzene rings in total) and 102 sp(2) -hybridized atoms. The synthesis involves sequential [4+2] and [2+2+2] aryne cycloadditions.

Tetracene Formation by On-Surface Reduction

We present the on-surface reduction of diepoxytetracenes to form genuine tetracene on Cu(111). The conversion is achieved by scanning tunneling microscopy (STM) tip-induced manipulation as well as thermal activation and is conclusively demonstrated by means of atomic force microscopy (AFM) with atomic resolution. We observe that the metallic surface plays an important role in the deoxygenation and for the planarization after bond cleavage.

Decacene: On-Surface Generation

Acenes are intriguing molecules with unique electronic properties. The difficulties in their preparation owing to low stability under ambient conditions are apparent because successful syntheses of long unsubstituted acenes are still scarce, in spite of the great attention they have attracted. Only unsubstituted acenes up to heptacene have been isolated in bulk, with nonacene being the largest acene detected to date. Herein we use on-surface assisted reduction of tetraepoxy decacene precursors on Au(111) as the key step to generate unprecedented decacene which is visualized and its electronic resonances studied by scanning tunneling microscopy (STM) and spectroscopy (STS).

Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort

Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.

Toward automatic generation of linguistic advice for saving energy at home

The increased demand of systems able to generate reports in natural language from numerical data involves the search for new solutions. This paper presents an adaptation of standard natural language generation methodologies to generate customized linguistic descriptions of data. Namely, we merge one of the most well-known architectures in the natural language generation research field together with our previous architecture for generating linguistic descriptions of complex phenomena. The latter is supported by the computational theory of perceptions which comes from the fuzzy sets and systems research field. We include a practical case of use dealing with the problem of inefficient consumption of energy at households. It generates natural language recommendations adapted to each household to promote a more responsible consumption. The proposal reveals opportunities of collaboration between the different research communities that are involved.

Generating automatic linguistic descriptions with big data

Dealing with Big Data, we have identified and analyzed seven issues: (1) scalability, (2) efficient processing, (3) incomplete and inaccurate data, (4) specific domains, (5) relevance of information, (6) levels of detail, and (7) intuitive and effective knowledge representation.We have developed a novel linguistic approach to deal with Big Data that fulfils these seven issues.We have provided an implementation of the paradigm Linguistic Descriptions of Complex Phenomena by applying MapReduce.We have taken advantage of Fuzzy Logic in order to manage incomplete and inaccurate Big Data. In highly connected world, the volume and variety of data is growing and growing. The Big Data era opens new challenges to address. Dealing with Big Data, we have identified and analyzed seven issues: (1) scalability, (2) efficient processing, (3) incomplete and inaccurate data, (4) specific domains, (5) relevance of information, (6) levels of detail, and (7) intuitive and effective knowledge representation. The analysis reveals that five of these issues are related to knowledge representation and human perception. Linguistic Descriptions of Complex Phenomena is a technology aimed to compute and generate linguistic reports customized to the user needs. In this paper, we present and describe an approach to Big Data based on this technology that faces the seven issues under study. Namely, we generate linguistic reports from Big Data that fulfill with the user requirements. To evaluate the generated linguistic reports we propose specific evaluation criteria based on the maxims of Grice. We illustrate the usefulness of the proposed solution by presenting a practical experiment based on the census data of the United States of America.

New types of computational perceptions: Linguistic descriptions in deforestation analysis

Abstract Automatic linguistic description of the available data about complex phenomena is a challenging task that is receiving the attention of data scientists in recent years. As an evolution of previous research results, there is a need of creating new linguistic computational models that allow us dealing with more complex phenomena and more complex descriptions of a growing amount of heterogeneous and real-time data. This paper contributes to this field by presenting three new ways of describing added-value information automatically extracted from data. Also, we extend previous computational models by including a description of the reliability of the available input data. Namely, we face this challenge by using a new implementation of the concept of Z-number proposed by Zadeh. We demonstrate the possibilities of the proposed extension with a practical application. The application generates automatic linguistic reports about the deforestation evolution in the Amazon region, e.g., “The deforestation last month was high. Because of the cloudiness, the reliability of this information is moderate”. Additionally, we evaluate the quality of the generated linguistic descriptions through fuzzy rating scale-based questionnaires. Moreover, we have also made a comparative study between reports generated with and without the new contributions introduced in this paper. The results show that the new types of computational perceptions introduced in this paper are ready to help data scientists to automatically generate good quality reports.

rLDCP: R package for text generation from data

The generation of text reports from numerical and symbolic data is getting the attention of many researchers. This paper presents an R package useful to develop computational systems able to generate linguistic descriptions of complex phenomena. It generates text reports from numerical and symbolic data related to the phenomena under consideration. This is an implementation of our previous research work that is supported by the computational theory of perceptions grounded in the fuzzy sets theory. Developing open source software while we follow the key issues (novelty, usability, interoperability, and relevance) will facilitate the adoption of this new discipline in the research community and industry. We present illustrative examples that show how to use this new R package. The examples reveal that the package is ready to become a relevant tool in the research field of text generation from data.

Fuzzy classifier ensembles for hierarchical WiFi-based semantic indoor localization

Combination of hierarchical localization and fuzzy classifier ensembles.Novel framework for WiFi-based semantic indoor localization.Validated in a real-world environment located at the University of Edinburgh.Mean error reduced more than a 55% compared to traditional fingerprint localization. The number of applications for smartphones and tablets is growing exponentially in the last years. Many of these applications are supported by the so-called Location Based Services, which are expected to provide reliable real-time localization anytime and anywhere, no matter either outdoors or indoors. Even though outdoors world-wide localization has been successfully developed through the well-known Global Navigation Satellite System technology, its counterpart large-scale deployment indoors is not available yet. In previous work, we have already introduced a novel technology for indoor localization supported by a WiFi fingerprint approach. In this paper, we describe how to enhance such approach through the combination of hierarchical localization and fuzzy classifier ensembles. It has been tested and validated at the University of Edinburgh, yielding promising results.

A Bibliometric Analysis of the Explainable Artificial Intelligence Research Field

This paper presents the results of a bibliometric study of the recent research on eXplainable Artificial Intelligence (XAI) systems. We took a global look at the contributions of scholars in XAI as well as in the subfields of AI that are mostly involved in the development of XAI systems. It is worthy to remark that we found out that about one third of contributions in XAI come from the fuzzy logic community. Accordingly, we went in depth with the actual connections of fuzzy logic contributions with AI to promote and improve XAI systems in the broad sense. Finally, we outlined new research directions aimed at strengthening the integration of different fields of AI, including fuzzy logic, toward the common objective of making AI accessible to people.